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primary topic: Comparison of principal component analysis and ANFIS to improve EEVE Laboratory energy use prediction performance

Comparison of principal component analysis and ANFIS to improve EEVE Laboratory energy use prediction performance
type: AcademicArticle
type: Article
abstract: The energy use that is in excess of practicum students’ needs and the disturbed comfort that the practicum students experience when conducting practicums in the Electrical eengineering vocational education (EEVE) laboratory. The main objective in this study was to figure out how to predict and streamline the use of electrical energy in the EEVE laboratory. The model used to achieve this research’s goal was called the adaptive neuro-fuzzy inference system (ANFIS) model, which was coupled with principal component analysis (PCA) feature selection. The use of PCA in data grouping performance aims to improve the performance of the ANFIS model when predicting energy needs in accordance with the standards set by the campus while still taking students’ confidence in conducting practicum activities during campus operating hours into consideration. After some experiments and tests, very good results were obtained in the training: R=1 in training; minimum RMSE=0.011900; epoch of 100 per iteration; and R=0.37522. In conclusion, the ANFIS model coupled with PCA feature selection was excellent at predicting energy needs in the laboratory while the comfort of the students during practicums in the room remained within consideration. © 2022 Institute of Advanced Engineering and Science. All rights reserved.
date: 2022
volume: 27
issue: 2
is part of: Indonesian Journal of Electrical Engineering and Computer Science
status: peerReviewed
status: published
author list: authors
type: EPrint
type: ArticleEPrint
is part of: repository

Institute of Advanced Engineering and Science
type: Organization

Indonesian Journal of Electrical Engineering and Computer Science
type: Collection
issn: 25024752

Norazhar Abu Bakar
type: Person
given name: Norazhar Abu
family name: Bakar

Mustofa Abi Hamid
type: Person
given name: Mustofa Abi
family name: Hamid

Nur Kholifah
type: Person
given name: Nur
family name: Kholifah

Muhammad Nurtanto
type: Person
given name: Muhammad
family name: Nurtanto

Romi Wiryadinata
type: Person
given name: Romi
family name: Wiryadinata

Desmira
type: Person
family name: Desmira

HTML Summary of #20465 Comparison of principal component analysis and ANFIS to improve EEVE Laboratory energy use prediction performance
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primary topic: Comparison of principal component analysis and ANFIS to improve EEVE Laboratory energy use prediction performance